Improving the resolution of retinal OCT with deep learning

In medical imaging, high-resolution can be crucial for identifying pathologies and subtle changes in tissue structure. However, in many scenarios, achieving high image resolution can be limited by physics or available technology. In this paper, we aim to develop an automatic and fast approach to increasing the resolution of Optical Coherence Tomography (OCT) images using the data available, without any additional information or repeated scans. We adapt a fully connected deep learning network for the super-resolution task, allowing multi-scale similarity to be considered, and create a training and testing set of more than 40,000 sample patches from retinal OCT data. Testing our model, we achieve an impressive root mean squared error of 5.847 and peak signal-to-noise ratio (PSNR) of 33.28 dB averaged over 8282 samples. This represents a mean improvement in PSNR of 3.2 dB over nearest neighbour and 1.4 dB over bilinear interpolation. The results achieved so far improve over commonly used fast techniques for increasing resolution and are very encouraging for further development towards fast OCT super-resolution. The ability to increase quickly the resolution of OCT as well as other medical images has the potential to impact significantly on medical imaging at point of care, allowing significant small details to be revealed efficiently and accurately for inspection by clinicians and graders and facilitating earlier and more accurate diagnosis of disease

Cytokine treatment optimises the immunotherapeutic effects of umbilical cord-derived MSC for treatment of inflammatory liver disease

Background: Mesenchymal stromal cells (MSC) possess immunomodulatory properties and low immunogenicity, both crucial properties for their development into an effective cellular immunotherapy. They have shown benefit in clinical trials targeting liver diseases; however the efficacy of MSC therapy will benefit from improvement of the immunomodulatory and immunogenic properties of MSC. Methods: MSC derived from human umbilical cords (ucMSC) were treated for 3 days in vitro with various inflammatory factors, interleukins, vitamins and serum deprivation. Their immunogenicity and immunomodulatory capacity were examined by gene-expression analysis, surface-marker expressions, IDO activity, PGE2 secretion and inhibition of T cell proliferation and IFNγ production. Furthermore, their activation of NK cell cytotoxicity was investigated via CD107a expre

Mouse mesenchymal stem cells inhibit high endothelial cell activation and lymphocyte homing to lymph nodes by releasing TIMP-1.

Mesenchymal stem cells (MSC) represent a promising therapeutic approach in many diseases in view of their potent immunomodulatory properties, which are only partially understood. Here, we show that the endothelium is a specific and key target of MSC during immunity and inflammation. In mice, MSC inhibit activation and proliferation of endothelial cells in remote inflamed lymph nodes (LNs), affect elongation and arborization of high endothelial venules (HEVs) and inhibit T-cell homing. The proteomic analysis of the MSC secretome identified the tissue inhibitor of metalloproteinase-1 (TIMP-1) as a potential effector molecule responsible for the anti-angiogenic properties of MSC. Both in vitro and in vivo, TIMP-1 activity is responsible for the anti-angiogenic effects of MSC, and increasing TIMP-1 concentrations delivered by an Adeno Associated Virus (AAV) vector recapitulates the effects of MSC transplantation on draining LNs. Thus, this study discovers a new and highly efficient general mechanism through which MSC tune down immunity and inflammation, identifies TIMP-1 as a novel biomarker of MSC-based therapy and opens the gate to new therapeutic approaches of inflammatory diseases

A Framework for Performance Evaluation of Face Recognition Algorithms

Face detection and recognition is becoming increasingly important in the contexts of surveillance, credit card fraud detection, assistive devices for visual impaired, etc. A number of face recognition algorithms have been proposed in the literature. The availability of a comprehensive face database is crucial to test the performance of these face recognition algorithms. However, while existing publicly-available face databases contain face images with a wide variety of poses angles, illumination angles, gestures, face occlusions, and illuminant colors, these images have not been adequately annotated, thus limiting their usefulness for evaluating the relative performance of face detection algorithms. For example, many of the images in existing databases are not annotated with the exact pose angles at which they were taken. In order to compare the performance of various face recognition algorithms presented in the literature there is a need for a comprehensive, systematically annotated database populated with face images that have been captured (1) at a variety of pose angles (to permit testing of pose invariance), (2) with a wide variety of illumination angles (to permit testing of illumination invariance), and (3) under a variety of commonly encountered illumination color temperatures (to permit testing of illumination color invariance). In this paper, we present a methodology for creating such an annotated database that employs a novel set of apparatus for the rapid capture of face images from a wide variety of pose angles and illumination angles. Four different types of illumination are used, including daylight, skylight, incandescent and fluorescent. The entire set of images, as well as the annotations and the experimental results, is being placed in the public domain, and made available for download over the worldwide web. 1

Validation of parameter estimation methods for determining optical properties of atherosclerotic tissues in intravascular OCT

In this paper we present a new process for assessing optical properties of tissues from 3D pullbacks, the standard clinical acquisition method for iOCT data. Our method analyzes a volume of interest (VOI) consisting of about 100 A-lines spread across the angle of rotation (\u3b8) and along the artery, z. The new 3D method uses catheter correction, baseline removal, speckle noise reduction, alignment of A-line sequences, and robust estimation. We compare results to those from a more standard, gold standard stationary acquisition where many image frames are averaged to reduce noise. To do these studies in a controlled fashion, we use a realistic optical artery phantom containing of multiple tissue types. Precision and accuracy for 3D pullback analysis are reported. Our results indicate that when implementing the process on a stationary acquisition dataset, the uncertainty improves at each stage while the uncertainty is reduced. When comparing stationary acquisition dataset to pullback dataset, the values were as follows: calcium: 3.8\ub11.09mm -1 in stationary and 3.9\ub11.2 mm-1 in a pullback; lipid: 11.025\ub10.417 mm-1 in stationary and 11.27\ub10.25 mm-1 in pullback; fibrous: 6.08\ub11.337 mm-1 in stationary and 5.58\ub12.0 mm-1. These results indicates that the process presented in this paper introduce minimal bias and only a small change in uncertainty when comparing a stationary and pullback dataset, thus paves the way to a highly accurate clinical plaque type discrimination, enabling automatic classification.Peer reviewed: YesNRC publication: Ye